Integration and characterisation of the performance of fifth-generation mobile technology (5g) connectivity over the University of Oulu 5g test network (5gtn) for cognitive edge node based on fractal edge platform

Abstract. In recent years, there has been a growing interest in cognitive edge nodes, which are intelligent devices that can collect and process data at the edge of the network. These nodes are becoming increasingly important for various applications such as smart cities, industrial automation, and healthcare. However, implementing cognitive edge nodes requires a reliable and efficient communication network. Therefore, this thesis assesses the performance of direct cellular (5G) and IEEE 802.11-based Wireless Local Area Network (WLAN) technology for three network architectures, which has the potential to offer low-latency, high-throughput and energy-efficient communication, for cognitive edge nodes. The study focused on evaluating the network performance metrics of throughput, latency, and power consumption for three different FRACTAL-based network architectures. These architectures include IEEE 802.11-based last mile, direct cellular (5G) backbone, and IEEE 802.11-based last mile over cellular (5G) backbone topologies. This research aims to provide insights into the performance of 5G technology for cognitive edge nodes. The findings suggest that the power consumption of IEEE 802.11-enabled nodes was only slightly higher than the reference case, indicating that it is more energy-efficient than 5G-enabled nodes. Additionally, in terms of latency, IEEE 802.11 technology may be more favourable. The throughput tests revealed that the cellular (5G) connection exhibited high throughput for communication between a test node and an upper-tier node situated either on the internet or at the network edge. In addition, it was found that the FRACTAL edge platform is flexible and scalable, and it supports different wireless technologies, making it a suitable platform for implementing cognitive edge nodes. Overall, this study provides insights into the potential of 5G technology and the FRACTAL edge platform for implementing cognitive edge nodes. The results of this research can be valuable for researchers and practitioners working in the field of wireless communication and edge computing, as it sheds light on the feasibility and performance of these technologies for implementing cognitive edge nodes in various applications

Delay measurements In live 5G cellular network

Abstract. 5G Network has many important properties, including increased bandwidth, increased data throughput, high reliability, high network density, and low latency. This thesis concentrate on the low latency attribute of the 5G Standalone (SA) mode and 5G Non-Standalone (NSA) mode. One of the most critical considerations in 5G is to have low latency network for various delay-sensitive applications, such as remote diagnostics and surgery in healthcare, self-driven cars, industrial factory automation, and live audio productions in the music industry. Therefore, 5G employs various retransmission algorithms and techniques to meet the low latency standards, a new frame structure with multiple subcarrier spacing (SCS) and time slots, and a new cloud-native core. For the low latency measurements, a test setup is built. A video is sent from the 5G User Equipment (UE) to the multimedia server deployed in the University of Oulu 5G test Network (5GTN) edge server. The University of Oulu 5GTN is operating both in NSA and SA modes. Delay is measured both for the downlink and the uplink direction with Qosium tool. When calculating millisecond-level transmission delays, clock synchronization is essential. Therefore, Precision Time Protocol daemon (PTPd) service is initiated on both the sending and receiving machines. The tests comply with the specifications developed at the University of Oulu 5GTN for both the SA and the NSA mode. When the delay measurement findings were compared between the two deployment modes, it was observed that the comparison was not appropriate. The primary reason for this is that in the 5GTN, the NSA and the SA have entirely different data routing paths and configurations. Additionally, the author did not have sufficient resources to make the required architectural changes

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

RF Energy Harvesting Techniques for Battery-less Wireless Sensing, Industry 4.0 and Internet of Things: A Review

As the Internet of Things (IoT) continues to expand, the demand for the use of energy-efficient circuits and battery-less devices has grown rapidly. Battery-less operation, zero maintenance and sustainability are the desired features of IoT devices in fifth generation (5G) networks and green Industry 4.0 wireless systems. The integration of energy harvesting systems, IoT devices and 5G networks has the potential impact to digitalize and revolutionize various industries such as Industry 4.0, agriculture, food, and healthcare, by enabling real-time data collection and analysis, mitigating maintenance costs, and improving efficiency. Energy harvesting plays a crucial role in envisioning a low-carbon Net Zero future and holds significant political importance. This survey aims at providing a comprehensive review on various energy harvesting techniques including radio frequency (RF), multi-source hybrid and energy harvesting using additive manufacturing technologies. However, special emphasis is given to RF-based energy harvesting methodologies tailored for battery-free wireless sensing, and powering autonomous low-power electronic circuits and IoT devices. The key design challenges and applications of energy harvesting techniques, as well as the future perspective of System on Chip (SoC) implementation, data digitization in Industry 4.0, next-generation IoT devices, and 5G communications are discussed

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version

Integration of a cellular Internet-of-Things transceiver into 6G test network and evaluation of its performance

Abstract. This thesis focuses on the integration and deployment of an aftermarket cellular IoT transceiver on a 6G/5G test network for the purpose of evaluating the feasibility of such device for monitoring the network performance. The cellular technology employed was NB-IoT paired with a Raspberry Pi device as the microprocessor that collects network telemetry and uses MQTT protocol to provide constant data feed. The system was first tested in a public cellular network through a local service provider and was successfully connected to the network, establishing TCP/IP connections, and allowing internet connectivity. To monitor network information and gathering basic telemetry data, a network monitoring utility was developed. It collected data such as network identifiers, module registration status, band/channel, signal strength and GPS position. This data was then published to a MQTT broker. The Adafruit IO platform served as the MQTT broker, providing an interface to visualize the collected data. Furthermore, the system was configured for and deployed on a 6G/5G test network successfully. The device functionality that was developed and tested in the public network remained intact, enabling continuous monitoring and analysis of network data. Through this study, valuable insights into the integration and deployment of cellular IoT transceivers into cellular networks that employ the latest IoT technology were gained. The findings highlight the feasibility of utilizing such a system for network monitoring and demonstrate the potential for IoT applications in cellular networks